Technical Field
The present invention relates generally to management of grid-scale Energy Storage Systems (ESSs), and more particularly, to a hybrid energy management system for dynamically controlling grid-scale ESSs for multiple services.
Description of the Related Art
Grid-connected energy storage systems (ESSs) are a fast growing global market. Recently, increases in the penetration of renewable energy resources into grid-connected ESSs have presented a challenge to the traditional design and operation of electric power systems. The existing power grid was designed for centralized power generation with unidirectional power flow. With renewable energy (or any other type of distributed generation of electricity), power is effectively generated everywhere and flows in multiple directions. However, the intermittent and highly variable nature of distributed generation causes power quality and/or reliability issues, which leads to increased energy costs.
Research on forecasting electricity prices has focused on techniques including employment of neural networks, principle component analysis, averaged Monte Carlo simulations, and time series modeling. Although these methods have been applied to obtain price forecasts, the focus of these methods is simply to improve forecasting quality through improved model fitting, and processing costs and the practical application of the forecasting information are not considered. Furthermore, these conventional forecasting methods also require large amounts of data (e.g., several months, years, etc.) for forecasting of electricity prices. Moreover, this forecasting is not employed for participation in energy markets.